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检索条件"任意字段=2012 IEEE Visual Communications and Image Processing, VCIP 2012"

共 124 条记录，以下是41-50 订阅

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SINGLE image SUPER-RESOLUTION VIA PHASE CONGRUENCY ANALYSIS

SINGLE IMAGE SUPER-RESOLUTION VIA PHASE CONGRUENCY ANALYSIS

引用

ieee International Conference on visual communications and image processing (vcip)

作者： Yu, Licheng Xu, Yi Zhang, Bo Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Key Lab Digital Media Proc & Commun Shanghai 200240 Peoples R China

ISBN: (纸本)9781479902880

Single image super-resolution (SR) is a severely unconstrained task. While the self-example-based methods are able to reproduce sharp edges, they perform poorly for textures. For recovering the fine details, higher-level image segmentation and corresponding external texture database are employed in the example-based SR methods, but they involve too much human interaction. In this paper, we discuss the existing problems of example-based technique using scale space analysis. Accordingly, a robust pixel classification method is designed based on the phase congruency model in scale space, which can effectively divide images into edges, textures and flat regions. Then a super-resolution framework is proposed, which can adaptively emphasize the importance of high-frequency residuals in structural examples and scale invariant fractal property in textural regions. Experimental results show that our SR approach is able to present both sharp edges and vivid textures with few artifacts.

关键词： Phase congruency quaternion Gabor super-resolution example-based synthesis fractal-based enhancement

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Mining Larger Class Activation Map with Common Attribute Labels

Mining Larger Class Activation Map with Common Attribute Lab...

引用

ieee International Conference on visual communications and image processing (vcip)

作者： Zhang, Runtong Meng, Fanman Li, Hongliang Wu, Qingbo Ngan, King N. Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781728180687

Class Activation Map (CAM) is the visualization of target regions generated from classification networks. However, classification network trained by class-level labels only has high responses to a few features of objects and thus the network cannot discriminate the whole target. We think that original labels used in classification tasks are not enough to describe all features of the objects. If we annotate more detailed labels like class-agnostic attribute labels for each image, the network may be able to mine larger CAM. Motivated by this idea, we propose and design common attribute labels, which are lower-level labels summarized from original image-level categories to describe more details of the target. Moreover, it should be emphasized that our proposed labels have good generalization on unknown categories since attributes (such as head, body, etc.) in some categories (such as dog, cat, etc.) are common and class-agnostic. That is why we call our proposed labels as common attribute labels, which are lower-level and more general compared with traditional labels. We finish the annotation work based on the PASCAL VOC2012 dataset and design a new architecture to successfully classify these common attribute labels. Then after fusing features of attribute labels into original categories, our network can mine larger CAMs of objects. Our method achieves better CAM results in visual and higher evaluation scores compared with traditional methods.

关键词： image classification class activation map common attribute label

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A novel in-loop filter based on CLDT masking effect model for HEVC

A novel in-loop filter based on CLDT masking effect model fo...

引用

2012 ieee visual communications and image processing, vcip 2012

作者： Luo, Binji Liu, Chonghua Zhang, Gong Ye, Feng Yang, Bo School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing 100876 China Beijing Institute of Spacecraft System Engineering Beijing 100094 China College of Mathematics and Computer Science Fujian Normal University Fuzhou 350001 China

ISBN: (纸本)9781467344050

HEVC is the current joint video coding standardization project of ITU-T Video Coding Experts Group and Moving Picture Experts Group. It includes deblocking filter (DF) and adaptive loop filter (ALF) after the deblocking filter. To improve the accuracy, the deblocking filter is modified with the parameter information of intra coded blocks in this paper. Considering that the masking effect of human visual system (HVS) will eliminate the visibility of the blocking artifacts, a combined luminance and directional-texture (CLDT) masking effect model-based DF is proposed. At last, a novel Wiener-based in-loop filter is proposed, which is applied to eliminate the quantization error using both pre-DF signal and post-DF signal. Experimental results show efficiency improvement and subjective quality improvement compared with HEVC test model 2 (HM2.0) anchor. © 2012 ieee.

关键词： image coding

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Hybrid Representation for 4D Medical image Compression

Hybrid Representation for 4D Medical Image Compression

引用

2024 Conference on visual communications and image processing

作者： Zheng, Wuyang Meng, Jiarui Zhang, Jiaqi Zhang, Jian Ma, Siwei Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Due to the substantial storage requirements of the 4D medical images, achieving efficient compression of such images is a crucial topic. Existing traditional image/video coding methods have achieved remarkable results in most compression tasks, but their performance in encoding 4D medical images remain poor. This is because these methods cannot fully exploit the spatio-temporal correlations in 4D images. Recently, implicit neural representation (INR) based image/video compression methods have made significant progress, with coding performance comparable to traditional methods. However, they also suffer from significant performance losses in 4D medical image compression like traditional methods. In this paper, we propose an efficient hybrid representation framework, which includes six learnable feature planes and a tiny MLP decoder. This framework alleviates the issue of previous methods lacking the ability to utilize the spatio-temporal correlations in 4D medical images, enabling it to capture these information more effectively. We also introduce a novel adaptive plane scaling strategy that allocates the numbers of parameter in each plane based on the resolution of the image. This design allows the model to further enhance the reconstruction quality at the same compression ratio. Extensive experiments show that our model achieves better RD performance compared to traditional and INR-based methods, and it also offers faster encoding speeds than INR-based methods.

关键词： Medical Data Compression Hybrid Representation Feature Planes

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visual communications and image processing Conference

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ieee CONSUMER ELECTRONICS MAGAZINE 2012年第3期1卷 50-+页

作者： [Anonymous]

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Packetization Strategies for MVD-based 3D Video Transmission

Packetization Strategies for MVD-based 3D Video Transmission

引用

30th ieee Conference on visual communications and image processing (vcip)

作者： Zhang, Xue Zhao, Yao Tillo, Tammam Lin, Chunyu Xiao, Jimin Wang, Anhong Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou Peoples R China Taiyuan Univ Sci & Technol Inst Digital Media & Commun Taiyuan Peoples R China

ISBN: (纸本)9781509053162

In multi-view video plus depth (MVD) format, virtual views are synthesized by the compressed texture videos and their associated depth through depth-image-based rendering. In this paper, we consider the setup where both the encoded texture and depth bitstreams experience packet losses during transmission. Different packetization strategies are investigated and a novel strategy is developed to improve error resilience of MVD-based video transmission, where texture data and its corresponding depth are put into the same packet. The size of texture plus associated depth data included in each packet needs to be less than the Maximum Transfer Unit (MTU). Experimental results demonstrate that our proposed packetization scheme yields a significant improvement in terms of both texture views and synthesized virtual views quality when fit in H.264/AVC.

关键词： image communication systems

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Multiscale Vision Transformer with Deep Clustering-Guided Refinement for Weakly Supervised Object Localization

Multiscale Vision Transformer with Deep Clustering-Guided Re...

引用

2023 ieee International Conference on visual communications and image processing, vcip 2023

作者： Kim, David Minkwan Cha, Sinhae Kang, Byeongkeun Hanyang University Department of Computer Science Seoul Korea Republic of Seoul National University of Science and Technology Department of Electronic Engineering Seoul Korea Republic of

ISBN: (纸本)9798350359855

This work addresses the task of weakly-supervised object localization. The goal is to learn object localization using only image-level class labels, which are much easier to obtain compared to bounding box annotations. This task is important because it reduces the need for labor-intensive ground-Truth annotations. However, methods for object localization trained using weak supervision often suffer from limited accuracy in localization. To address this challenge and enhance localization accuracy, we propose a multiscale object localization transformer (MOLT). It comprises multiple object localization transformers that extract patch embeddings across various scales. Moreover, we introduce a deep clustering-guided refinement method that further enhances localization accuracy by utilizing separately extracted image segments. These segments are obtained by clustering pixels using convolutional neural networks. Finally, we demonstrate the effectiveness of our proposed method by conducting experiments on the publicly available ILSVRC-2012 dataset. © 2023 ieee.

关键词： image enhancement

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A Near Lossless Learned image Coding Network Quantization Approach for Cross-Platform Inference

A Near Lossless Learned Image Coding Network Quantization Ap...

引用

2023 ieee International Conference on visual communications and image processing, vcip 2023

作者： Hang, Xinyu Jia, Chuanmin Ma, Siwei Gao, Wen Peking University National Engineering Research Center on Visual Technology School of Computer Science China Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9798350359855

This paper presents an innovative pipeline design for the quantization of neural image codecs, ensuring robust and consistent deployment across various platform inferences. Our approach addresses a significant challenge prevalent in neural image compression networks: the lack of quantization methods for entropy probability hyper-priors, such as Gaussian mixture models (GMMs), in the existing literature. Our method serves as a solution to prevent decoding errors stemming from float-point discrepancies between the encoder and decoder, especially when executed on different platforms. By implementing a quantization scheme based on look-up tables (LUTs), we achieve precise quantization of GMM probability models with minimal performance degradation, approaching near losslessness. Moreover, our contribution offers a comprehensive quantization pipeline that facilitates dependable data transmission and harmonious synchronization of arithmetic coding algorithms between the encoder and decoder components. Empirical evidence highlights the effectiveness of our method, demonstrating seamless codec matching on diverse platforms and substantiating its capacity to enhance the integrity and security of compressed data. This work enhances the practical applicability of neural compression techniques across real-world scenarios. © 2023 ieee.

关键词： Table lookup

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Reducing HEVC encoding complexity using two-stage motion estimation

Reducing HEVC encoding complexity using two-stage motion est...

引用

visual communications and image processing, vcip 2015

作者： Cebrian-Marquez, Gabriel Chi, Chi Ching Martinez, Jose Luis Cuenca, Pedro Alvarez-Mesa, Mauricio Sanz-Rodriguez, Sergio Juurlink, Ben University of Castilla-La Mancha Albacete Spain Technische Universität Berlin Berlin Germany

ISBN: (纸本)9781467373142

We propose a technique for optimizing the High Efficiency Video Coding (HEVC) encoder by reducing the number of operations performed in the motion estimation stage. The technique is based on the fact that a significant number of motion estimation operations are performed repetitively for the same image samples, but for different block partition sizes. By decoupling the initial motion estimation and the block partitioning into different stages it is possible to remove a considerable number of redundant motion estimation operations. An implementation of the proposed technique on a SIMD optimized version of the HEVC reference encoder shows that, on average, a reduction of 79.02% SAD operations can be achieved, that results in an average reduction of 14.63% of the encoding complexity with negligible impact on the compression efficiency (BD-rate losses of less than 1%). © 2015 ieee.

关键词： image compression

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Hybrid Implicit Neural image Compression with Subpixel Context Model and Iterative Pruner

Hybrid Implicit Neural Image Compression with Subpixel Conte...

引用

2023 ieee International Conference on visual communications and image processing, vcip 2023

作者： Li, Hangyu Jiang, Wei Li, Litian Zhai, Yongqi Wang, Ronggang Peking University Shenzhen Graduate School Shenzhen China Pengcheng Laboratory Shenzhen China

ISBN: (纸本)9798350359855

Recently, image compression based on Implicit Neural Representation (INR) has gained interest. However, the performance of INR-based methods is poor because MLP can not fit high-frequency information well. Recently, a kind of hybrid INR method was proposed, which transmits not only the parameters of MLP but also some external latents to help to overfit at the decoder. Despite its low MACs (Multiply-Accumulate Operations per pixel), the decoding time is still slow due to the serial autoregressive context model for entropy coding. Hence, we propose two methods to improve the hybrid INR method's decoding speed and performance. First, the Subpixel Context Model (SAM) is aimed to speed up the process of entropy coding. The proposed SAM is latent by latent instead of pixel by pixel. Second, we add an iterative pruner to the pipeline of hybrid INR compression, to further reduce the bit rate. The performance and MACs of our approach are on par with the SOTA of the hybrid INR methods. The decoding speed is about eight times faster on CPU, and three times faster on GPU. © 2023 ieee.

关键词： image compression

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